DEG-analysis

workflowr

• Summary
• Checks
• Past versions

Last updated: 2024-02-02

Checks: 7 0

Knit directory: UPF1-FMR1/

This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

---

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20230923)

The command set.seed(20230923) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 1d1c97e

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 1d1c97e. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

Ignored files:
    Ignored:    .Rproj.user/
    Ignored:    analysis/Exploratory-DEG.nb.html
    Ignored:    analysis/Quality-control.nb.html
    Ignored:    analysis/figure/

Untracked files:
    Untracked:  data/lists/LCL-gene-list/

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/DEG-analysis.Rmd) and HTML (docs/DEG-analysis.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	1d1c97e	urwahnawaz	2024-02-02	wflow_publish(c("analysis/DEG-analysis.Rmd", "analysis/index.Rmd"))
html	9dd04b8	urwahnawaz	2024-02-01	Build site.
Rmd	3265f59	urwahnawaz	2024-02-01	wflow_publish(c("analysis/DEG-analysis.Rmd", "analysis/index.Rmd"))
Rmd	3b20b67	urwahnawaz	2024-01-24	wflow_git_commit("./*")
html	b6a1d60	urwahnawaz	2024-01-22	Build site.
html	98b3ae9	urwahnawaz	2024-01-18	Build site.
Rmd	4f57b71	urwahnawaz	2024-01-18	wflow_publish(c("analysis/index.Rmd", "analysis/DEG-analysis.Rmd"))
html	2b88aaf	urwahnawaz	2024-01-17	Build site.
html	144afef	urwahnawaz	2024-01-17	Build site.
Rmd	068b751	urwahnawaz	2024-01-17	wflow_git_commit("./*")
html	facdd9d	urwahnawaz	2024-01-17	Build site.
html	6cb16fa	urwahnawaz	2024-01-17	Build site.
Rmd	e0ee067	urwahnawaz	2024-01-17	wflow_publish("analysis/DEG-analysis.Rmd")
html	2b5790f	urwahnawaz	2024-01-12	Build site.
Rmd	bd34cfd	urwahnawaz	2024-01-12	wflow_publish("analysis/DEG-analysis.Rmd")

---

Introduction

source(here::here("code/libraries.R"))
library(stargazer)
library(ggfortify)
library(glue)
library(cowplot)
library(broom)
library(glmpca)
library(naniar)
library(gridExtra)
library(EnsDb.Hsapiens.v86)
library(ggrepel)
library(org.Hs.eg.db)
library(msigdbr)
library(fgsea)

goSummaries <- url("https://uofabioinformaticshub.github.io/summaries2GO/data/goSummaries.RDS") %>%
    readRDS() %>%
    mutate(ontology = as.character(ontology))

getGeneLists <- function(pwf, goterms, genome, ids){
    gene2cat <- getgo(rownames(pwf), genome, ids)
    cat2gene <- split(rep(names(gene2cat), sapply(gene2cat, length)),
                      unlist(gene2cat, use.names = FALSE))
    out <- list()
    for(term in goterms){
        tmp <- pwf[cat2gene[[term]],]
        tmp <- rownames(tmp[tmp$DEgenes > 0, ])
        out[[term]] <- tmp
    }
    out


}

txdf = ensembldb::transcripts(EnsDb.Hsapiens.v86, return.type="DataFrame")
tx2gene = as.data.frame(txdf[,c("tx_id","gene_id", "tx_biotype")])

gene2symbol = ensembldb::genes(EnsDb.Hsapiens.v86, return.type="DataFrame") %>% 
  as.data.frame() %>% 
  dplyr::select(gene_id, gene_name)

ah <- AnnotationHub() %>%
    subset(species == "Homo sapiens") %>%
    subset(rdataclass == "EnsDb") %>% 
  subset(genome == "GRCh38")

ensDb <- ah[["AH109606"]]
grTrans <- transcripts(ensDb)
trLengths <- exonsBy(ensDb, "tx") %>%
    width() %>%
    vapply(sum, integer(1))
mcols(grTrans)$length <- trLengths[names(grTrans)]

genesGR = ensembldb::genes(ensDb)
transGR = transcripts(ensDb)

mcols(transGR) = mcols(transGR) %>%
  cbind(
    transcriptLengths(ensDb)[rownames(.), c("nexon", "tx_len")]
  )


id2Name <- structure(
  genesGR$gene_name,
  names = genesGR$gene_id
) %>% 
  .[!duplicated(names(.))]

salmon.files = ("/home/neuro/Documents/NMD_analysis/Analysis/Results/UPF1-FMR1/Salmon")
salmon = list.files(salmon.files, pattern = "transcripts$", full.names = TRUE)
all_files = file.path(salmon, "quant.sf")
sample_names = gsub("/home/neuro/Documents/NMD_analysis/Analysis/Results/UPF1-FMR1/Salmon/", "", salmon)
sample_names = gsub(".gz_transcripts", "", sample_names)
sample_names = gsub("\\_.*", "", sample_names)
names(all_files) <- sample_names
md = read.csv(here::here("data/Sample_info.csv"), header= TRUE) %>%  
  #mutate(files = file.path(salmon, "quant.sf")) %>% 
  dplyr::rename("names" = "GeneWiz.ID", 
                "Group" = "Sample.type") %>% 
  mutate(Group = ifelse(Group == "MC" | Group == "FC", "Control", Group)) %>%
  dplyr::select(names,everything()) %>% 
  mutate(names = gsub("\\_.*", "", names) )
md = md[order(match(md$names, sample_names)),]
md %<>% 
  rownames_to_column("random") %>% 
#  column_to_rownames("names") %>%
  dplyr::select(-random) %>%
  mutate(files = all_files)

md %<>% dplyr::filter(Group != "FMR1") %>%
 dplyr::filter(names != "23-LDJ6767") %>%
dplyr::filter(names != "202")
all_files = all_files[names(all_files) %in% md$names]

txi_genes =  tximport(all_files, type="salmon", txOut=FALSE,
                      countsFromAbundance="scaledTPM", tx2gene = tx2gene, ignoreTxVersion = TRUE, ignoreAfterBar = TRUE)
keep.genes = (rowSums(txi_genes$abundance >= 1 ) >= 3)

Differential gene expression analysis

txi_genes_filtered = txi_genes$counts[keep.genes,]
y <- DGEList(txi_genes_filtered)


design <- model.matrix(~Batch +Group + Sex, data = md) %>% 
    set_colnames(gsub(pattern = "Group", replacement ="", x = colnames(.)))

y <- calcNormFactors(y)
v <- voom(y, design)

fit = lmFit(v, design) %>% 
    eBayes()

summary(decideTests(fit, lfc =0))

       (Intercept) Batch  FRAX  UPF1  SexM
Down           398   439   533   233    11
NotSig        2189 12093 11814 12551 13189
Up           10627   682   867   430    14

UPF1 DEGs

upf1_results_lfc = topTable(fit,coef = "UPF1", number = Inf) %>% 
                     mutate(res = ifelse(logFC > 0& adj.P.Val < 0.05, "Upregulated", 
                      ifelse(logFC < 0 & adj.P.Val < 0.05, "Downregulated", "NotSig"))) %>% 
  rownames_to_column("ensembl_gene_id") %>%
   mutate(SYMBOL = mapIds(org.Hs.eg.db, keys=ensembl_gene_id,  column="SYMBOL",keytype="ENSEMBL", multiVals="first")) %>%
  mutate(res = ifelse(logFC > 0 & adj.P.Val < 0.05, "Upregulated", 
                      ifelse(logFC < 0  & adj.P.Val < 0.05, "Downregulated", "NotSig")))

DEColours <- c("Downregulated" = "#2e294e","Upregulated" = "#720026",  "NotSig" = "#E5E5E5")


volc_upf1 = upf1_results_lfc  %>%
    ggplot(aes(y = -log10(adj.P.Val), 
               x =  logFC , 
               colour = res,
               size =-log10(adj.P.Val), 
               label= SYMBOL)) +
  geom_point(alpha = 0.8) +
  # geom_text(aes(label=ifelse(SYMBOL== "Upf1",as.character(SYMBOL),''))) +
  #  geom_text(aes(label= SYMBOL), subset = SYMBOL == "Upf1") +
    scale_colour_manual(values = DEColours) + theme_classic() + 
    theme(axis.title.y = element_text(size = 12)) +
    geom_hline(yintercept = -log10(0.05), color = "grey60", size = 0.5, lty = "dashed") +
    labs(x = "log2 Fold Change", y = "-log10 adj p-value") +
  geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") +
  xlim(-8.5, 8.5) + ylim(0, 7.5)


# volc = volc_upf1+ geom_text_repel(data=subset(upf1_results_lfc , SYMBOL %in% c("UPF1", "UPF2", "UPF3B",
#                       "SMG5", "SMG6",
#                       "UPF3A", "ATF4", 
#                       "GADD5G")),
#             aes(label=SYMBOL),   position=position_dodge(width = 0.9), 
#              vjust=-0.40, color = "black", box.padding = 0.5, fill = "white") + ggtitle("DEGs in UPF1 relative to controls using limma/voom")
# 
# 
# volc 

my_gg = volc_upf1 + geom_point_interactive(aes(tooltip =SYMBOL, data_id = SYMBOL), 
    size = 1, hover_nearest = TRUE)
girafe(ggobj = my_gg)

Volcano plot showing distribution of differentially expressed genes using the limma/voom pipeline

upf1_results_lfc %>% 
  dplyr::filter(adj.P.Val < 0.05) %>%
  dplyr::select(SYMBOL,ensembl_gene_id, logFC, adj.P.Val) %>% 
  dplyr::arrange(adj.P.Val) %>% 
  DT::datatable(caption="DEGS in UPF1 relative to controls using limma/voom pipeline") 

• Gene ontology enrichment analysis

Gene tonic for visualisation

library(GeneTonic)
library(DESeq2)
library(AnnotationDbi)
library(pcaExplorer)
library(igraph)
library(visNetwork)
library(magrittr)
library(topGO)

tbledger <- as.data.frame(upf1_results_lfc)
colnames(tbledger)[colnames(tbledger) == "P.Value"] <- "pvalue"
colnames(tbledger)[colnames(tbledger) == "logFC"] <- "log2FoldChange"
colnames(tbledger)[colnames(tbledger) == "AveExpr"] <- "baseMean"
colnames(tbledger)[colnames(tbledger) == "SYMBOL"] <- "SYMBOL"
rownames(tbledger) =tbledger$ensembl_gene_id
tbledger$baseMean <- (2^tbledger$baseMean) * mean(y$samples$lib.size) / 1e6

edger_resu <- DESeqResults(DataFrame(tbledger))
edger_resu <- DESeq2:::pvalueAdjustment(edger_resu,
                                        independentFiltering = FALSE,
                                        alpha = FDR, pAdjustMethod = "BH")

anno_df <- data.frame(
  gene_id = upf1_results_lfc$ensembl_gene_id,
  gene_name = mapIds(org.Hs.eg.db, keys = upf1_results_lfc$ensembl_gene_id, column = "SYMBOL", keytype = "ENSEMBL"),
  stringsAsFactors = FALSE,
  row.names = upf1_results_lfc$ensembl_gene_id
)

sig_upf1 =upf1_results_lfc %>% 
  dplyr::filter(adj.P.Val < 0.05) 

topgo_all_upf1_bp <-
  pcaExplorer::topGOtable(sig_upf1$SYMBOL,
                          upf1_results_lfc$SYMBOL,
                          ontology = "BP",
                          mapping = "org.Hs.eg.db",
                          geneID = "symbol",
                          topTablerows = 500,
                          do_padj = TRUE) %>%
  as.data.frame() %>%
  mutate(Ont = "BP") %>%
  dplyr::filter(p.value_classic < 0.05)


topgo_all_upf1_bp <- shake_topGOtableResult(topgo_all_upf1_bp)

topgo_all_upf1_bp <- get_aggrscores(res_enrich = topgo_all_upf1_bp ,
                                        res_de = edger_resu,
                                        annotation_obj = anno_df,
                                        aggrfun = mean)

em <- enrichment_map(topgo_all_upf1_bp,
                     edger_resu,
                     n_gs = 30,
                     color_by = "z_score",
                     anno_df)

graph_vis = em %>% 
  visIgraph() %>% 
  visOptions(highlightNearest = list(enabled = TRUE,
                                     degree = 1,
                                     hover = TRUE),
             nodesIdSelection = TRUE)

graph_vis 

library(plotly)
p <- enhance_table(topgo_all_upf1_bp,
                   edger_resu,
                   n_gs = 30,
                   annotation_obj = anno_df,
                   chars_limit = 60)
ggplotly(p)

distilled <- distill_enrichment(topgo_all_upf1_bp,
                                edger_resu,
                                anno_df,
                                n_gs = Inf,
                                cluster_fun = "cluster_markov")

DT::datatable(distilled$res_enrich[,])

• Upregulated genes

sig_upf1 =upf1_results_lfc %>% 
  dplyr::filter(adj.P.Val < 0.05 & logFC > 0 ) 

topgo_all_upf1_bp <-
  pcaExplorer::topGOtable(sig_upf1$SYMBOL,
                          upf1_results_lfc$SYMBOL,
                          ontology = "BP",
                          mapping = "org.Hs.eg.db",
                          geneID = "symbol",
                          topTablerows = 500, 
                          do_padj = TRUE) %>%
  as.data.frame() %>%
  mutate(Ont = "BP") %>%
  dplyr::filter(p.value_classic < 0.05)

topgo_all_upf1_bp <- shake_topGOtableResult(topgo_all_upf1_bp)

topgo_all_upf1_bp  <- get_aggrscores(res_enrich = topgo_all_upf1_bp ,
                                        res_de = edger_resu,
                                        annotation_obj = anno_df,
                                        aggrfun = mean)

em <- enrichment_map(topgo_all_upf1_bp,
                     edger_resu,
                     n_gs = 50,
                     color_by = "z_score",
                     anno_df)

graph_vis = em %>% 
  visIgraph() %>% 
  visOptions(highlightNearest = list(enabled = TRUE,
                                     degree = 1,
                                     hover = TRUE),
             nodesIdSelection = TRUE)

graph_vis 

p <- enhance_table(topgo_all_upf1_bp,
                   edger_resu,
                   n_gs = 30,
                   annotation_obj = anno_df,
                   chars_limit = 60)
ggplotly(p)

distilled <- distill_enrichment(topgo_all_upf1_bp,
                                edger_resu,
                                anno_df,
                                n_gs = Inf,
                                cluster_fun = "cluster_markov")

DT::datatable(distilled$res_enrich[,])

• Downregulated genes

sig_upf1 =upf1_results_lfc %>% 
  dplyr::filter(adj.P.Val < 0.05 & logFC < 0 ) 

topgo_all_upf1_bp <-
  pcaExplorer::topGOtable(sig_upf1$SYMBOL,
                          upf1_results_lfc$SYMBOL,
                          ontology = "BP",
                          mapping = "org.Hs.eg.db",
                          geneID = "symbol",
                          topTablerows = 500, 
                          do_padj = TRUE) %>%
  as.data.frame() %>%
  mutate(Ont = "BP") %>%
  dplyr::filter(p.value_classic < 0.05)

topgo_all_upf1_bp <- shake_topGOtableResult(topgo_all_upf1_bp)

topgo_all_upf1_bp  <- get_aggrscores(res_enrich = topgo_all_upf1_bp ,
                                        res_de = edger_resu,
                                        annotation_obj = anno_df,
                                        aggrfun = mean)

em <- enrichment_map(topgo_all_upf1_bp,
                     edger_resu,
                     n_gs = 50,
                     color_by = "z_score",
                     anno_df)

graph_vis = em %>% 
  visIgraph() %>% 
  visOptions(highlightNearest = list(enabled = TRUE,
                                     degree = 1,
                                     hover = TRUE),
             nodesIdSelection = TRUE)

graph_vis 

p <- enhance_table(topgo_all_upf1_bp,
                   edger_resu,
                   n_gs = 30,
                   annotation_obj = anno_df,
                   chars_limit = 60)
ggplotly(p)

distilled <- distill_enrichment(topgo_all_upf1_bp,
                                edger_resu,
                                anno_df,
                                n_gs = Inf,
                                cluster_fun = "cluster_markov")

DT::datatable(distilled$res_enrich[,])

FRAX DEGs

frax_results_lfc = topTable(fit,coef = "FRAX", number = Inf) %>% 
                     mutate(res = ifelse(logFC > 0  & adj.P.Val < 0.05, "Upregulated", 
                      ifelse(logFC < 0 & adj.P.Val < 0.05, "Downregulated", "NotSig"))) %>% 
  rownames_to_column("ensembl_gene_id") %>%
   mutate(SYMBOL = mapIds(org.Hs.eg.db, keys=ensembl_gene_id,  column="SYMBOL",keytype="ENSEMBL", multiVals="first")) %>% 
    mutate(res = ifelse(logFC > 0 & adj.P.Val < 0.05, "Upregulated", 
                      ifelse(logFC < 0  & adj.P.Val < 0.05, "Downregulated", "NotSig"))) 

save(frax_results_lfc,upf1_results_lfc, file = here::here("output/DEG-results.Rda"))

DEColours <- c("Downregulated" = "#2e294e","Upregulated" = "#720026",  "NotSig" = "#E5E5E5")


volc_frax = frax_results_lfc  %>%
    ggplot(aes(y = -log10(adj.P.Val), 
               x =  logFC , 
               colour = res,
               size =-log10(adj.P.Val), 
               label= SYMBOL)) +
  geom_point(alpha = 0.8) +
  # geom_text(aes(label=ifelse(SYMBOL== "Upf1",as.character(SYMBOL),''))) +
  #  geom_text(aes(label= SYMBOL), subset = SYMBOL == "Upf1") +
    scale_colour_manual(values = DEColours) + theme_classic() + 
    theme(axis.title.y = element_text(size = 12)) +
    geom_hline(yintercept = -log10(0.05), color = "grey60", size = 0.5, lty = "dashed") +
    labs(x = "log2 Fold Change", y = "-log10 adj p-value") +
  geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") +
  xlim(-8.5, 8.5) + ylim(0, 7.5)


# volc = volc + geom_text_repel(data=subset(upf1_results_lfc , SYMBOL %in% c("UPF1", "UPF2", "UPF3B",
#                       "SMG5", "SMG6",
#                       "UPF3A", "ATF4", 
#                       "GADD5G", "FMR1")),
#             aes(label=SYMBOL),   position=position_dodge(width = 0.9), 
#              vjust=-0.40, color = "black", box.padding = 0.5, fill = "white") + ggtitle("DEGs in FRAX relative to controls using limma/voom pipeline")


my_gg =  volc_frax + geom_point_interactive(aes(tooltip =SYMBOL, data_id = SYMBOL), 
    size = 1, hover_nearest = TRUE)
girafe(ggobj = my_gg)

Enrichment analysis

tbledger <- as.data.frame(frax_results_lfc)
colnames(tbledger)[colnames(tbledger) == "P.Value"] <- "pvalue"
colnames(tbledger)[colnames(tbledger) == "logFC"] <- "log2FoldChange"
colnames(tbledger)[colnames(tbledger) == "AveExpr"] <- "baseMean"
colnames(tbledger)[colnames(tbledger) == "SYMBOL"] <- "SYMBOL"
rownames(tbledger) =tbledger$ensembl_gene_id
tbledger$baseMean <- (2^tbledger$baseMean) * mean(y$samples$lib.size) / 1e6

edger_resu <- DESeqResults(DataFrame(tbledger))
edger_resu <- DESeq2:::pvalueAdjustment(edger_resu,
                                        independentFiltering = FALSE,
                                        alpha = FDR, pAdjustMethod = "BH")

anno_df <- data.frame(
  gene_id = frax_results_lfc$ensembl_gene_id,
  gene_name = mapIds(org.Hs.eg.db, keys = frax_results_lfc$ensembl_gene_id, column = "SYMBOL", keytype = "ENSEMBL"),
  stringsAsFactors = FALSE,
  row.names = frax_results_lfc$ensembl_gene_id
)

sig_frax=frax_results_lfc %>% 
  dplyr::filter(adj.P.Val < 0.05) 

topgo_all_frax <-
  pcaExplorer::topGOtable(sig_frax$SYMBOL,
                         frax_results_lfc$SYMBOL,
                          ontology = "BP",
                          mapping = "org.Hs.eg.db",
                          geneID = "symbol",
                          topTablerows = 500,
                          do_padj = TRUE) %>%
  as.data.frame() %>%
  mutate(Ont = "BP") %>%
  dplyr::filter(p.value_classic < 0.05)


topgo_all_frax<- shake_topGOtableResult(topgo_all_frax)

topgo_all_frax<- get_aggrscores(res_enrich = topgo_all_frax ,
                                        res_de = edger_resu,
                                        annotation_obj = anno_df,
                                        aggrfun = mean)

em <- enrichment_map(topgo_all_frax,
                     edger_resu,
                     n_gs = 30,
                     color_by = "z_score",
                     anno_df)

graph_vis = em %>% 
  visIgraph() %>% 
  visOptions(highlightNearest = list(enabled = TRUE,
                                     degree = 1,
                                     hover = TRUE),
             nodesIdSelection = TRUE)

graph_vis 

library(plotly)
p <- enhance_table(topgo_all_frax,
                   edger_resu,
                   n_gs = 30,
                   annotation_obj = anno_df,
                   chars_limit = 60)
ggplotly(p)

distilled <- distill_enrichment(topgo_all_frax,
                                edger_resu,
                                anno_df,
                                n_gs = Inf,
                                cluster_fun = "cluster_markov")

DT::datatable(distilled$res_enrich[,])

• Upregulated genes

sig_frax=frax_results_lfc %>% 
  dplyr::filter(adj.P.Val < 0.05 & logFC > 0 ) 

topgo_all_frax <-
  pcaExplorer::topGOtable(sig_frax$SYMBOL,
                         frax_results_lfc$SYMBOL,
                          ontology = "BP",
                          mapping = "org.Hs.eg.db",
                          geneID = "symbol",
                          topTablerows = 500,
                          do_padj = TRUE) %>%
  as.data.frame() %>%
  mutate(Ont = "BP") %>%
  dplyr::filter(p.value_classic < 0.05)


topgo_all_frax<- shake_topGOtableResult(topgo_all_frax)

topgo_all_frax<- get_aggrscores(res_enrich = topgo_all_frax ,
                                        res_de = edger_resu,
                                        annotation_obj = anno_df,
                                        aggrfun = mean)

em <- enrichment_map(topgo_all_frax,
                     edger_resu,
                     n_gs = 50,
                     color_by = "z_score",
                     anno_df)

graph_vis = em %>% 
  visIgraph() %>% 
  visOptions(highlightNearest = list(enabled = TRUE,
                                     degree = 1,
                                     hover = TRUE),
             nodesIdSelection = TRUE)

graph_vis 

p <- enhance_table(topgo_all_frax,
                   edger_resu,
                   n_gs = 30,
                   annotation_obj = anno_df,
                   chars_limit = 60)
ggplotly(p)

distilled <- distill_enrichment(topgo_all_frax,
                                edger_resu,
                                anno_df,
                                n_gs = Inf,
                                cluster_fun = "cluster_markov")

DT::datatable(distilled$res_enrich[,])

• Downregulated genes

sig_frax=frax_results_lfc %>% 
  dplyr::filter(adj.P.Val < 0.05 & logFC < 0 ) 

topgo_all_frax <-
  pcaExplorer::topGOtable(sig_frax$SYMBOL,
                         frax_results_lfc$SYMBOL,
                          ontology = "BP",
                          mapping = "org.Hs.eg.db",
                          geneID = "symbol",
                          topTablerows = 500,
                          do_padj = TRUE) %>%
  as.data.frame() %>%
  mutate(Ont = "BP") %>%
  dplyr::filter(p.value_classic < 0.05)


topgo_all_frax<- shake_topGOtableResult(topgo_all_frax)

topgo_all_frax<- get_aggrscores(res_enrich = topgo_all_frax ,
                                        res_de = edger_resu,
                                        annotation_obj = anno_df,
                                        aggrfun = mean)

em <- enrichment_map(topgo_all_frax,
                     edger_resu,
                     n_gs = 50,
                     color_by = "z_score",
                     anno_df)

graph_vis = em %>% 
  visIgraph() %>% 
  visOptions(highlightNearest = list(enabled = TRUE,
                                     degree = 1,
                                     hover = TRUE),
             nodesIdSelection = TRUE)

graph_vis 

p <- enhance_table(topgo_all_frax,
                   edger_resu,
                   n_gs = 30,
                   annotation_obj = anno_df,
                   chars_limit = 60)
ggplotly(p)

distilled <- distill_enrichment(topgo_all_frax,
                                edger_resu,
                                anno_df,
                                n_gs = Inf,
                                cluster_fun = "cluster_markov")

DT::datatable(distilled$res_enrich[,])

Overlap of UPF1 and FRAX

• Number of genes overlapping

library(VennDiagram)
x= list("UPF1 DEGs" = upf1_results_lfc$SYMBOL[upf1_results_lfc$adj.P.Val < 0.05], 
        "FRAX DEGs" = frax_results_lfc$SYMBOL[frax_results_lfc$adj.P.Val < 0.05])

ggvenn(x)

Past versions of unnamed-chunk-43-1.png

Version	Author	Date
9dd04b8	urwahnawaz	2024-02-01

overlaps = calculate.overlap(x)
upf1_only = overlaps$a1[!overlaps$a1 %in% overlaps$a3]
fmr1_only = overlaps$a2[!overlaps$a2 %in% overlaps$a3]

upf1_results_lfc %>% 
  dplyr::filter(adj.P.Val < 0.05) %>%
  dplyr::select(SYMBOL,ensembl_gene_id, logFC, adj.P.Val) %>% 
  inner_join(frax_results_lfc %>% 
               dplyr::filter(adj.P.Val < 0.05) %>%
                dplyr::select(ensembl_gene_id, FRAX.logFC=logFC, 
                              FRAX.padj =adj.P.Val), by = "ensembl_gene_id") %>% 
  ggscatter(., x="logFC", y = "FRAX.logFC", cor.coef = TRUE, add = "reg.line", 
              size=5, alpha =0.6, 
              conf.int = TRUE, add.params = list(color = "#EF3829",
                                                 fill = "lightgray")) + 
    theme_bw() + ylab("FRAX (log2FoldChange)") + xlab("UPF1 (log2FoldChange)") +
  geom_hline(yintercept = 0, size = 0.5,lty = "dashed", color = "grey60") +
  geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") + ggtitle("Direction of expression of the 222 overlapping genes")

Overlap of significant genes from FRAX and UPF1

Past versions of unnamed-chunk-45-1.png

Version	Author	Date
9dd04b8	urwahnawaz	2024-02-01

• Gene ontology of overlapping genes

sig_genes =overlaps$a3

topgo_all_frax <-
  pcaExplorer::topGOtable(sig_genes,
                         frax_results_lfc$SYMBOL,
                          ontology = "BP",
                          mapping = "org.Hs.eg.db",
                          geneID = "symbol",
                          topTablerows = 500,
                          do_padj = TRUE) %>%
  as.data.frame() %>%
  mutate(Ont = "BP") %>%
  dplyr::filter(p.value_classic < 0.05)


topgo_all_frax<- shake_topGOtableResult(topgo_all_frax)

topgo_all_frax<- get_aggrscores(res_enrich = topgo_all_frax ,
                                        res_de = edger_resu,
                                        annotation_obj = anno_df,
                                        aggrfun = mean)

em <- enrichment_map(topgo_all_frax,
                     edger_resu,
                     n_gs = 50,
                     color_by = "z_score",
                     anno_df)

graph_vis = em %>% 
  visIgraph() %>% 
  visOptions(highlightNearest = list(enabled = TRUE,
                                     degree = 1,
                                     hover = TRUE),
             nodesIdSelection = TRUE)

graph_vis 

p <- enhance_table(topgo_all_frax,
                   edger_resu,
                   n_gs = 30,
                   annotation_obj = anno_df,
                   chars_limit = 60)
ggplotly(p)

distilled <- distill_enrichment(topgo_all_frax,
                                edger_resu,
                                anno_df,
                                n_gs = Inf,
                                cluster_fun = "cluster_markov")

DT::datatable(distilled$res_enrich[,])

• UPF1 specific genes

upf1_results_lfc %>% 
  dplyr::filter(SYMBOL %in% upf1_only) %>% 
  dplyr::select(SYMBOL,ensembl_gene_id, logFC, adj.P.Val) %>% 
  inner_join(frax_results_lfc %>% 
                dplyr::select(ensembl_gene_id, FRAX.logFC=logFC, 
                              FRAX.padj =adj.P.Val), by = "ensembl_gene_id") %>%
  ggscatter(., x="logFC", y = "FRAX.logFC", cor.coef = TRUE, add = "reg.line", 
              size=5, alpha =0.6, 
              conf.int = TRUE, add.params = list(color = "#EF3829",
                                                 fill = "lightgray")) + 
    theme_bw() + ylab("FRAX (log2FoldChange)") + xlab("UPF1 (log2FoldChange)") +
  geom_hline(yintercept = 0, size = 0.5,lty = "dashed", color = "grey60") +
  geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") + ggtitle("Direction of expression of the 441 UPF1 specific genes")

Past versions of unnamed-chunk-50-1.png

Version	Author	Date
9dd04b8	urwahnawaz	2024-02-01

topgo_all_frax <-
  pcaExplorer::topGOtable(upf1_only,
                         frax_results_lfc$SYMBOL,
                          ontology = "BP",
                          mapping = "org.Hs.eg.db",
                          geneID = "symbol",
                          topTablerows = 500,
                          do_padj = TRUE) %>%
  as.data.frame() %>%
  mutate(Ont = "BP") %>%
  dplyr::filter(p.value_classic < 0.05)


topgo_all_frax<- shake_topGOtableResult(topgo_all_frax)

topgo_all_frax<- get_aggrscores(res_enrich = topgo_all_frax ,
                                        res_de = edger_resu,
                                        annotation_obj = anno_df,
                                        aggrfun = mean)

em <- enrichment_map(topgo_all_frax,
                     edger_resu,
                     n_gs = 50,
                     color_by = "z_score",
                     anno_df)
graph_vis = em %>% 
  visIgraph() %>% 
  visOptions(highlightNearest = list(enabled = TRUE,
                                     degree = 1,
                                     hover = TRUE),
             nodesIdSelection = TRUE)

graph_vis 

p <- enhance_table(topgo_all_frax,
                   edger_resu,
                   n_gs = 30,
                   annotation_obj = anno_df,
                   chars_limit = 60)
ggplotly(p)

distilled <- distill_enrichment(topgo_all_frax,
                                edger_resu,
                                anno_df,
                                n_gs = Inf,
                                cluster_fun = "cluster_markov")

DT::datatable(distilled$res_enrich[,])

• FRAX specific genes

frax_results_lfc %>% 
  dplyr::filter(SYMBOL %in% fmr1_only) %>% 
  dplyr::select(SYMBOL,ensembl_gene_id, FRAX.logFC=logFC, 
                              FRAX.padj =adj.P.Val) %>% 
  inner_join(upf1_results_lfc %>% 
                dplyr::select(ensembl_gene_id, logFC, 
                            adj.P.Val), by = "ensembl_gene_id") %>%
  ggscatter(., x="logFC", y = "FRAX.logFC", cor.coef = TRUE, add = "reg.line", 
              size=5, alpha =0.6, 
              conf.int = TRUE, add.params = list(color = "#EF3829",
                                                 fill = "lightgray")) + 
    theme_bw() + ylab("FRAX (log2FoldChange)") + xlab("UPF1 (log2FoldChange)") +
  geom_hline(yintercept = 0, size = 0.5,lty = "dashed", color = "grey60") +
  geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") + ggtitle("Direction of expression of the 1178 FRAX specific genes")

Past versions of unnamed-chunk-55-1.png

Version	Author	Date
9dd04b8	urwahnawaz	2024-02-01

topgo_all_frax <-
  pcaExplorer::topGOtable(fmr1_only,
                         frax_results_lfc$SYMBOL,
                          ontology = "BP",
                          mapping = "org.Hs.eg.db",
                          geneID = "symbol",
                          topTablerows = 500,
                          do_padj = TRUE) %>%
  as.data.frame() %>%
  mutate(Ont = "BP") %>%
  dplyr::filter(p.value_classic < 0.05)


topgo_all_frax<- shake_topGOtableResult(topgo_all_frax)

topgo_all_frax<- get_aggrscores(res_enrich = topgo_all_frax ,
                                        res_de = edger_resu,
                                        annotation_obj = anno_df,
                                        aggrfun = mean)

em <- enrichment_map(topgo_all_frax,
                     edger_resu,
                     n_gs = 50,
                     color_by = "z_score",
                     anno_df)
graph_vis = em %>% 
  visIgraph() %>% 
  visOptions(highlightNearest = list(enabled = TRUE,
                                     degree = 1,
                                     hover = TRUE),
             nodesIdSelection = TRUE)

graph_vis 

p <- enhance_table(topgo_all_frax,
                   edger_resu,
                   n_gs = 30,
                   annotation_obj = anno_df,
                   chars_limit = 60)
ggplotly(p)

distilled <- distill_enrichment(topgo_all_frax,
                                edger_resu,
                                anno_df,
                                n_gs = Inf,
                                cluster_fun = "cluster_markov")

DT::datatable(distilled$res_enrich[,])

Enrichment analysis with other NMD LCLs

lists = list()
lists$upf3b = read_excel(here::here("data/lists/LCL-gene-list/UPF3B-variants.xlsx")) %>% as.data.frame() %>% 
  dplyr::rename("gene_name"= "external_gene_name") %>%
  left_join(gene2symbol, by = "gene_name") %>% 
  dplyr::select(log2FoldChan, padj, "EnsID" = gene_id , "symbol" = gene_name)


lists$upf2_group = read_excel(here::here("data/lists/LCL-gene-list/UPF2-CNV-grouped.xlsx")) %>% 
  as.data.frame() %>% 
  dplyr::select(log2FoldChang, padj, EnsID, "symbol" = external_gene_name)

lists$upf2_frameshift= read_excel(here::here("data/lists/LCL-gene-list/UPF2-frameshift-case.xlsx")) %>% as.data.frame() %>% 
  dplyr::select(log2FoldChange, padj, EnsID , "symbol" = external_gene_name)

lists$smg = read_excel(here::here("data/lists/LCL-gene-list/SMG-LCLs.xlsx")) %>% as.data.frame() %>%
  mutate_at(.vars = "EnsID", .funs = gsub, pattern = "\\.[0-9]*$", replacement = "")  %>% 
  dplyr::select(log2FoldChange, padj, EnsID, symbol)

Over-representation test

Transcript feature analysis

library(GeneOverlap)
overlaps_sig = lapply(lists, function(x){
  sig = x %>% 
    dplyr::filter(padj < 0.05)
  bg = calculate.overlap( x = list("x" = x$EnsID,
                         "upf1" = upf1_results_lfc$ensembl_gene_id))
  bg = bg$a3
  upf1_sig = upf1_results_lfc %>%
    dplyr::filter(adj.P.Val < 0.05)
    go.obj <- newGeneOverlap(sig$EnsID,
                         upf1_sig$ensembl_gene_id,
                         genome.size=length(upf1_results_lfc$ensembl_gene_id))
     go.obj <- testGeneOverlap(go.obj)
     enrichment_nif_pval= go.obj@pval
     enrichment_nif_OR= go.obj@odds.ratio
      NIF_feature_enrichment = cbind(enrichment_nif_pval, enrichment_nif_OR)
      return(NIF_feature_enrichment)
})

overlap_sig_frax = lapply(lists, function(x){
  sig = x %>% 
    dplyr::filter(padj < 0.05)
  bg = calculate.overlap( x = list("x" = x$EnsID,
                         "frax" = frax_results_lfc$ensembl_gene_id))
  bg = bg$a3
  frax_sig = frax_results_lfc %>%
    dplyr::filter(adj.P.Val < 0.05)
    go.obj <- newGeneOverlap(sig$EnsID,
                         frax_sig$ensembl_gene_id,
                         genome.size=length(frax_results_lfc$ensembl_gene_id))
     go.obj <- testGeneOverlap(go.obj)
     enrichment_nif_pval= go.obj@pval
     enrichment_nif_OR= go.obj@odds.ratio
      NIF_feature_enrichment = cbind(enrichment_nif_pval, enrichment_nif_OR)
      return(NIF_feature_enrichment)
})

frax_sig = frax_results_lfc %>%
    dplyr::filter(adj.P.Val < 0.05)

upf1_sig = upf1_results_lfc %>%
    dplyr::filter(adj.P.Val < 0.05)
go.obj <- newGeneOverlap(upf1_sig$ensembl_gene_id,
                         frax_sig$ensembl_gene_id,
                         genome.size=length(upf1_results_lfc$ensembl_gene_id))
go.obj <- testGeneOverlap(go.obj)
enrichment_nif_pval= go.obj@pval
enrichment_nif_OR= go.obj@odds.ratio
NIF_feature_enrichment = cbind(enrichment_nif_pval, enrichment_nif_OR)

overlaps_sig$frax = NIF_feature_enrichment

Correlation with UPF1 DEGs

do.call(rbind, overlaps_sig) %>% 
  as.data.frame() %>%
  mutate(overlap = c("UPF3B LCL DEGs", 
                     "UPF2 CNVs LCL DEGs", 
                     "UPF2 Frameshift LCLs", 
                     "SMG8/9 LCLs DEGs", 
                     "FRAX LCLs DEGs")) %>%
ggplot(aes(x=enrichment_nif_OR, y = overlap)) + geom_bar(stat = "identity", fill = "#edafb8") +
  theme_bw() + geom_vline(xintercept = 1) + 
   theme(axis.text.x = element_text(size = 12, family = "serif", color = "black"),
            axis.text.y = element_text(size = 12, family = "serif", color = "black"),
        axis.title = element_text(size=12, family = "serif"), 
         legend.box.background = element_rect(color = "black"),
          legend.text = element_text(family = "serif"), 
          legend.title = element_text( family = "serif"),
           plot.title = element_text(family = "serif", size =20),         
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_line( size=.1 ),
              legend.position = "top",
        strip.text.y = element_text(
        size = 10, face = "bold.italic", family = "serif"
        ),
         strip.text.x = element_text(
        size = 10, family = "serif"
        )) + labs(x="Odds ratio", 
                  y = "") + 
  annotate("label", x =0.5, y = 5, label = "8.38-02") +
   annotate("label", x =0.5, y = 4, label = "4.6e-03") +
  annotate("label", x =0.5, y = 3, label = " 6.7e-01") +
  annotate("label", x =0.5, y = 2, label = " 3.97e-01 ") +
  annotate("label", x =0.5, y = 1, label = " 9.79e-61")

UPF1 and UPF3B DEGs

upf3b_cor = upf1_results_lfc %>% 
  dplyr::filter(adj.P.Val < 0.05) %>%
  dplyr::select(SYMBOL,"EnsID" = ensembl_gene_id, logFC, adj.P.Val) %>% 
  inner_join(lists$upf3b %>% 
               dplyr::filter( padj < 0.05) %>%
                dplyr::select(EnsID, UPF3B.logFC=log2FoldChan), 
             by = "EnsID") %>% 
  ggscatter(., x="logFC", y = "UPF3B.logFC", cor.coef = TRUE, add = "reg.line", 
              size=5, alpha =0.6, 
              conf.int = TRUE, add.params = list(color = "#EF3829",
                                                 fill = "lightgray")) + 
    theme_bw() + ylab("UPF3B LCLs (log2FoldChange)") + xlab("UPF1 (log2FoldChange)") +
  geom_hline(yintercept = 0, size = 0.5,lty = "dashed", color = "grey60") +
  geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") + ggtitle("Direction of expression of the 21 overlapping genes")

my_gg = upf3b_cor  + geom_point_interactive(aes(tooltip =SYMBOL, data_id = SYMBOL), 
    size = 1, hover_nearest = TRUE)
girafe(ggobj = my_gg)

UPF1 and UPF2 CNVs DEGs

upf2_cor = upf1_results_lfc %>% 
  dplyr::filter(adj.P.Val < 0.05) %>%
  dplyr::select(SYMBOL,"EnsID" = ensembl_gene_id, logFC, adj.P.Val) %>% 
  inner_join(lists$upf2_group %>% 
               dplyr::filter( padj < 0.05) %>%
                dplyr::select(EnsID, UPF2.logFC=log2FoldChang), 
             by = "EnsID") %>% 
  ggscatter(., x="logFC", y = "UPF2.logFC", cor.coef = TRUE, add = "reg.line", 
              size=5, alpha =0.6, 
              conf.int = TRUE, add.params = list(color = "#EF3829",
                                                 fill = "lightgray")) + 
    theme_bw() + ylab("UPF2 CNVs LCLs (log2FoldChange)") + xlab("UPF1 (log2FoldChange)") +
  geom_hline(yintercept = 0, size = 0.5,lty = "dashed", color = "grey60") +
  geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") + ggtitle("Direction of expression of the 36 overlapping genes")

my_gg = upf2_cor + geom_point_interactive(aes(tooltip =SYMBOL, data_id = SYMBOL), 
    size = 1, hover_nearest = TRUE)
girafe(ggobj = my_gg)

UPF1 and UPF2 Frameshift DEGs

upf2_fs = upf1_results_lfc %>% 
  dplyr::filter(adj.P.Val < 0.05) %>%
  dplyr::select(SYMBOL,"EnsID" = ensembl_gene_id, logFC, adj.P.Val) %>% 
  inner_join(lists$upf2_frameshift %>% 
               dplyr::filter( padj < 0.05) %>%
                dplyr::select(EnsID, UPF2.logFC=log2FoldChange), 
             by = "EnsID") %>% 
  ggscatter(., x="logFC", y = "UPF2.logFC", cor.coef = TRUE, add = "reg.line", 
              size=5, alpha =0.6, 
              conf.int = TRUE, add.params = list(color = "#EF3829",
                                                 fill = "lightgray")) + 
    theme_bw() + ylab("UPF2 Frameshift LCL (log2FoldChange)") + xlab("UPF1 (log2FoldChange)") +
  geom_hline(yintercept = 0, size = 0.5,lty = "dashed", color = "grey60") +
  geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") + ggtitle("Direction of expression of the 5 overlapping genes")

my_gg = upf2_fs  + geom_point_interactive(aes(tooltip =SYMBOL, data_id = SYMBOL), 
    size = 1, hover_nearest = TRUE)
girafe(ggobj = my_gg)

UPF1 DEGs and SMG8/9 LCLs

smg_cor = upf1_results_lfc %>% 
  dplyr::filter(adj.P.Val < 0.05) %>%
  dplyr::select(SYMBOL,"EnsID" = ensembl_gene_id, logFC, adj.P.Val) %>% 
  inner_join(lists$smg %>% 
               dplyr::filter( padj < 0.05) %>%
                dplyr::select(EnsID, SMG.logFC=log2FoldChange), 
             by = "EnsID") %>% 
  ggscatter(., x="logFC", y = "SMG.logFC", cor.coef = TRUE, add = "reg.line", 
              size=5, alpha =0.6, 
              conf.int = TRUE, add.params = list(color = "#EF3829",
                                                 fill = "lightgray")) + 
    theme_bw() + ylab("SMG8/9 (log2FoldChange)") + xlab("UPF1 (log2FoldChange)") +
  geom_hline(yintercept = 0, size = 0.5,lty = "dashed", color = "grey60") +
  geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") + ggtitle("Direction of expression of the 124 overlapping genes")

my_gg = smg_cor + geom_point_interactive(aes(tooltip =SYMBOL, data_id = SYMBOL), 
    size = 1, hover_nearest = TRUE)
girafe(ggobj = my_gg)

Correlation with FRAX DEGs

do.call(rbind, overlap_sig_frax) %>% 
  as.data.frame() %>%
  mutate(overlap = c("UPF3B LCL DEGs", 
                     "UPF2 CNVs LCL DEGs", 
                     "UPF2 Frameshift LCLs", 
                     "SMG8/9 LCLs DEGs")) %>%
ggplot(aes(x=enrichment_nif_OR, y = overlap)) + geom_bar(stat = "identity", fill = "#edafb8") +
  theme_bw() + geom_vline(xintercept = 1) + 
   theme(axis.text.x = element_text(size = 12, family = "serif", color = "black"),
            axis.text.y = element_text(size = 12, family = "serif", color = "black"),
        axis.title = element_text(size=12, family = "serif"), 
         legend.box.background = element_rect(color = "black"),
          legend.text = element_text(family = "serif"), 
          legend.title = element_text( family = "serif"),
           plot.title = element_text(family = "serif", size =20),         
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_line( size=.1 ),
              legend.position = "top",
        strip.text.y = element_text(
        size = 10, face = "bold.italic", family = "serif"
        ),
         strip.text.x = element_text(
        size = 10, family = "serif"
        )) + labs(x="Odds ratio", 
                  y = "") + 
  annotate("label", x =0.25, y = 4, label = " 0.99") +
   annotate("label", x =0.25, y = 3, label = " 0.65") +
  annotate("label", x =0.25, y = 2, label = " 0.99") +
  annotate("label", x =0.25, y = 1, label = "  0.44") 

Data export

Session information

sessionInfo()

R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

locale:
 [1] LC_CTYPE=en_AU.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8    
 [5] LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8   
 [7] LC_PAPER=en_AU.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C       

time zone: Australia/Adelaide
tzcode source: system (glibc)

attached base packages:
 [1] grid      stats4    tools     stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] GeneOverlap_1.38.0          VennDiagram_1.7.3          
 [3] futile.logger_1.4.3         plotly_4.10.4              
 [5] topGO_2.54.0                SparseM_1.81               
 [7] GO.db_3.18.0                visNetwork_2.1.2           
 [9] igraph_1.6.0                pcaExplorer_2.28.0         
[11] GeneTonic_2.6.0             fgsea_1.28.0               
[13] msigdbr_7.5.1               org.Hs.eg.db_3.18.0        
[15] ggrepel_0.9.5               EnsDb.Hsapiens.v86_2.99.0  
[17] gridExtra_2.3               naniar_1.0.0               
[19] glmpca_0.2.0                broom_1.0.5                
[21] cowplot_1.1.2               glue_1.7.0                 
[23] ggfortify_0.4.16            stargazer_5.2.3            
[25] ngsReports_2.4.0            patchwork_1.2.0            
[27] AnnotationHub_3.10.0        BiocFileCache_2.10.1       
[29] dbplyr_2.4.0                openxlsx_4.2.5.2           
[31] ggiraph_0.8.8               DT_0.31                    
[33] msigdb_1.10.0               GSEABase_1.64.0            
[35] graph_1.80.0                annotate_1.80.0            
[37] XML_3.99-0.16               pheatmap_1.0.12            
[39] ggvenn_0.1.10               MetBrewer_0.2.0            
[41] ggpubr_0.6.0                venn_1.12                  
[43] viridis_0.6.4               viridisLite_0.4.2          
[45] tximeta_1.20.2              tximport_1.30.0            
[47] goseq_1.54.0                geneLenDataBase_1.38.0     
[49] BiasedUrn_2.0.11            org.Mm.eg.db_3.18.0        
[51] EnsDb.Mmusculus.v79_2.99.0  ensembldb_2.26.0           
[53] AnnotationFilter_1.26.0     GenomicFeatures_1.54.1     
[55] AnnotationDbi_1.64.1        biomaRt_2.58.0             
[57] edgeR_4.0.11                limma_3.58.1               
[59] DESeq2_1.42.0               SummarizedExperiment_1.32.0
[61] Biobase_2.62.0              MatrixGenerics_1.14.0      
[63] matrixStats_1.2.0           GenomicRanges_1.54.1       
[65] GenomeInfoDb_1.38.5         IRanges_2.36.0             
[67] S4Vectors_0.40.2            BiocGenerics_0.48.1        
[69] corrplot_0.92               lubridate_1.9.3            
[71] forcats_1.0.0               purrr_1.0.2                
[73] readr_2.1.5                 tidyverse_2.0.0            
[75] stringr_1.5.1               tidyr_1.3.0                
[77] scales_1.3.0                data.table_1.14.10         
[79] readxl_1.4.3                tibble_3.2.1               
[81] magrittr_2.0.3              reshape2_1.4.4             
[83] ggplot2_3.4.4               dplyr_1.1.4                
[85] workflowr_1.7.1            

loaded via a namespace (and not attached):
  [1] vroom_1.6.5                   progress_1.2.3               
  [3] Biostrings_2.70.1             vctrs_0.6.5                  
  [5] digest_0.6.34                 png_0.1-8                    
  [7] shape_1.4.6                   shinyBS_0.61.1               
  [9] registry_0.5-1                git2r_0.33.0                 
 [11] MASS_7.3-60.0.1               httpuv_1.6.13                
 [13] foreach_1.5.2                 withr_3.0.0                  
 [15] xfun_0.41                     ellipsis_0.3.2               
 [17] survival_3.5-7                memoise_2.0.1                
 [19] systemfonts_1.0.5             gtools_3.9.5                 
 [21] zoo_1.8-12                    GlobalOptions_0.1.2          
 [23] prettyunits_1.2.0             KEGGREST_1.42.0              
 [25] promises_1.2.1                httr_1.4.7                   
 [27] rstatix_0.7.2                 restfulr_0.0.15              
 [29] ps_1.7.6                      rstudioapi_0.15.0            
 [31] shinyAce_0.4.2                miniUI_0.1.1.1               
 [33] generics_0.1.3                base64enc_0.1-3              
 [35] processx_3.8.3                babelgene_22.9               
 [37] curl_5.2.0                    zlibbioc_1.48.0              
 [39] ca_0.71.1                     polyclip_1.10-6              
 [41] GenomeInfoDbData_1.2.11       SparseArray_1.2.3            
 [43] RBGL_1.78.0                   threejs_0.3.3                
 [45] interactiveDisplayBase_1.40.0 xtable_1.8-4                 
 [47] doParallel_1.0.17             evaluate_0.23                
 [49] S4Arrays_1.2.0                hms_1.1.3                    
 [51] colorspace_2.1-0              filelock_1.0.3               
 [53] Rgraphviz_2.46.0              shinyWidgets_0.8.1           
 [55] later_1.3.2                   lattice_0.22-5               
 [57] NMF_0.26                      genefilter_1.84.0            
 [59] getPass_0.2-4                 pillar_1.9.0                 
 [61] nlme_3.1-164                  iterators_1.0.14             
 [63] caTools_1.18.2                gridBase_0.4-7               
 [65] compiler_4.3.2                stringi_1.8.3                
 [67] shinycssloaders_1.0.0         Category_2.68.0              
 [69] TSP_1.2-4                     dendextend_1.17.1            
 [71] GenomicAlignments_1.38.2      plyr_1.8.9                   
 [73] crayon_1.5.2                  abind_1.4-5                  
 [75] BiocIO_1.12.0                 ggdendro_0.1.23              
 [77] locfit_1.5-9.8                bit_4.0.5                    
 [79] fastmatch_1.1-4               whisker_0.4.1                
 [81] codetools_0.2-19              crosstalk_1.2.1              
 [83] bslib_0.6.1                   GetoptLong_1.0.5             
 [85] mime_0.12                     splines_4.3.2                
 [87] circlize_0.4.15               Rcpp_1.0.12                  
 [89] tippy_0.1.0                   cellranger_1.1.0             
 [91] knitr_1.45                    blob_1.2.4                   
 [93] utf8_1.2.4                    here_1.0.1                   
 [95] clue_0.3-65                   BiocVersion_3.18.1           
 [97] fs_1.6.3                      backbone_2.1.2               
 [99] admisc_0.34                   expm_0.999-9                 
[101] ggsignif_0.6.4                Matrix_1.6-5                 
[103] callr_3.7.3                   statmod_1.5.0                
[105] tzdb_0.4.0                    visdat_0.6.0                 
[107] tweenr_2.0.2                  pkgconfig_2.0.3              
[109] cachem_1.0.8                  RSQLite_2.3.5                
[111] DBI_1.2.1                     fastmap_1.1.1                
[113] rmarkdown_2.25                shinydashboard_0.7.2         
[115] Rsamtools_2.18.0              sass_0.4.8                   
[117] BiocManager_1.30.22           carData_3.0-5                
[119] farver_2.1.1                  mgcv_1.9-1                   
[121] AnnotationForge_1.44.0        yaml_2.3.8                   
[123] rtracklayer_1.62.0            cli_3.6.2                    
[125] webshot_0.5.5                 lifecycle_1.0.4              
[127] lambda.r_1.2.4                backports_1.4.1              
[129] rintrojs_0.3.4                BiocParallel_1.36.0          
[131] timechange_0.3.0              gtable_0.3.4                 
[133] rjson_0.2.21                  ggridges_0.5.5               
[135] parallel_4.3.2                jsonlite_1.8.8               
[137] colourpicker_1.3.0            seriation_1.5.4              
[139] bitops_1.0-7                  assertthat_0.2.1             
[141] bit64_4.0.5                   zip_2.3.0                    
[143] heatmaply_1.5.0               bs4Dash_2.3.0                
[145] futile.options_1.0.1          highr_0.10                   
[147] jquerylib_0.1.4               lazyeval_0.2.2               
[149] pander_0.6.5                  shiny_1.8.0                  
[151] dynamicTreeCut_1.63-1         htmltools_0.5.7              
[153] formatR_1.14                  rappdirs_0.3.3               
[155] XVector_0.42.0                RCurl_1.98-1.14              
[157] rprojroot_2.0.4               ComplexUpset_1.3.3           
[159] R6_2.5.1                      gplots_3.1.3                 
[161] labeling_0.4.3                cluster_2.1.6                
[163] rngtools_1.5.2                DelayedArray_0.28.0          
[165] tidyselect_1.2.0              ProtGenerics_1.34.0          
[167] GOstats_2.68.0                ggforce_0.4.1                
[169] xml2_1.3.6                    car_3.1-2                    
[171] KernSmooth_2.23-22            munsell_0.5.0                
[173] htmlwidgets_1.6.4             ComplexHeatmap_2.18.0        
[175] RColorBrewer_1.1-3            rlang_1.1.3                  
[177] uuid_1.2-0                    fansi_1.0.6